Multivariate distributions can be compared by means of statistical tests which
seek to identify differences in ensemble means. These tests are generally based on the
assumption that both joint distributions are Gaussian, which may not
be true. If a very small number of observations are all that can be
found from the experiment and a joint distribution can be fit to
statistics from many realizations of the model, then it is also
possible to test how unlikely it is that the experimental observations
came from the model distribution. This type of test is risky, since
the observations in a small experimental sample may be unrepresentative. A third method of comparison is based on statistical
learning, also known as data mining [1]}. A classification rule is
constructed with the aim of being able to distinguish replicates
of the experiment from realizations from the model. If a subset of variables can be
found which can be used to build an effective classification rule,
then these variables may identify differences between the model and the experiment.
